针对散乱点云数据密度大、重建时间长、效率低等问题，提出了一种散乱点云的均匀精简算法。该算法基于开源C++编程库点云库(PCL)，利用PCL的体素化栅格类创建一个K邻域三维体素栅格，结合包围盒法对输入的点云数据进行K邻域距离计算和法线估计，确定每个小立方栅格的重心，并以其来近似显示这个小立方栅格内所有的数据点，达到精简点云的目的，最后利用贪婪三角投影类对精简后的点云实现三角网格面重建并显示其效果。实验结果表明，该算法在充分保留点云数据几何特征的前提下，能有效滤除部分点云数据冗余量，且精简结果比较均匀，避免了大规模精简所出现的空白区域，提高了重建效率。

Aiming at the problems of high density, long reconstruction time and low reconstruction efficiency for scattered point cloud data, a new uniform simplification algorithm for scattered point cloud data is proposed. This algorithm is based on the open-source C++ programming library point cloud library (PCL). Firstly, a K-nearest neighborhood voxel grid is built by voxel grid class in PCL. Next, according to the bounding box algorithm the K-nearest neighborhood distance of the point cloud data is calculated and the normal of the point cloud data is estimated. Then the barycenter of each small voxel grid is established, which replaces all point cloud data in the voxel grid to achieve point cloud simplification. Finally, the simplified point cloud data is reconstructed and displayed with triangular mesh by greedy projection triangulation class. The experimental results show that in the premise of fully retaining geometric characteristics of point cloud data, the proposed algorithm can effectively remove partial redundancy of the point cloud data and simplify the data uniformly without large-scale blank area, and the reconstruction efficiency is improved.